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Department of Natural Resources

Economic & Revenue Forecast

Fiscal Year 2018, Second QuarterNovember 2017

Forecast Summary

Lumber and Log Prices. After peaking at an aver-age or $376/mbf in 2014, west coast lumber pricesfell to $317/mbf for 2015. They recovered slightly in2016, averaging $341/mbf, mostly due to higher firstquarter housing starts than in 2015. The increasein starts spiked lumber demand, catching lumberdealers off-guard, and pushed prices up from theend of the first quarter. Prices retreated toward theend of the year but did not fall to earlier lows. Lum-ber prices for 2017 have been significantly higher,averaging $406/mbf through September.

Through 2015 a ‘typical’ DNR log averaged$521/mbf, falling from the $591/mbf average in2014. The average price for 2016 was slightly higherat $536/mbf. The decline in 2015 was primarily dueto the dramatic slowdown in demand from Chinaand to an ample regional supply of both logs andlumber. Log prices have increased through 2017,primarily due to increased lumber demand; they’veaveraged $592/mbf through September. Prices areexpected to remain high through early 2018.

Timber Sales Volume. Given current timber salesplans, the sales volume forecast for FY 18 is un-changed as 500 mmbf. Sales plans in outlyingyears have not changed, so absent a new sustain-able harvest calculation, sales volume forecasts inthose years also remain at 500 mmbf.

Timber Sales Prices. FY 17 auction prices aver-aged $345/mbf. To-date, auction prices for FY 18have averaged $439 with around 20 percent of theforecast volumen sold. The sales price forecastsFY 18 remains at $363/mbf. The sales price fore-casts for outlying years are also unchanged.

Timber Removal Volume and Prices. Account-ing for changes to purchaser plans and the timingof contract expirations, FY 18 harvest volume ex-pectations are lowered by 50 mmbf to 589 mmbf.This volume is being pushed out to FYs 19, 20, and21, increasing them slightly to 597, 532, and 515mmbf, respectively.

The average timber removal price for FY 18 is low-ered to $318/mbf. Timber removal prices for FYs19-21 are projected to be about $341 (+$2), $346 (-

$5), and $343 (+$1) per mbf. These removal pricesreflect changes in the removal timing.

Timber Revenue. The above changes to timbersales prices, sales volumes, and harvest timing haveshifted projected revenue in all forecast years. Rev-enues for the 2017-2019 biennium are forecast tototal $388 million, down around three percent ($11million) from Septembers’s forecast. Forecast rev-enues for the 2019-2021 biennium are increased bythree percent ($10 million) to $361 million.

Uplands and Aquatic Lands Lease (Non-Timber) Revenues. In addition to revenue fromtimber removals on state-managed lands, DNR alsogenerates sizable revenues from managing leases onuplands and aquatic lands.

The upland lease revenue forecast for FY 18 is in-creased by $1 million due to increases in revenueexpectations for irrigated and orchard/vineyardleases and commercial property leases, which out-weigh decreased expectations for other leases.Revenue forecasts for outlying years are un-changed.

Aquatic lease revenue expectations are reducedby a small amount for all forecast years due tocontinually lower expectations for water-dependentleases.

The FY 18 geoduck revenue is increased by $2 mil-lion to $22 million due to a higher planned salesvolume than previously forecast. The revenue fore-casts for the outlying years are unchanged.

Total Revenues. Total revenue for the 2017-2019Biennium (FYs 18-19) are decreased by $8 million,to $533 million. Revenues for the 2019-2021 Bien-nium (FYs 20 and 21) are raised by $10 million to$502 million.

Notes to the Forecast. While the sales volumeestimates are based on the best available internalplanning data, they are subject to adjustments dueto ongoing operational and policy issues. In partic-ular, these issues are likely to affect sales volumes inoutlying years, where the assumed sustainable har-vest volume of 500 mmbf might be too high.

A continuing downside risk for the forecast is tim-ber and lumber demand from China, which has al-

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ready experienced a steep decline, could drop evenfurther if the country’s economic growth continuesto slow down.

In previous forecasts, we noted that the expirationof the Softwood Lumber Agreement posed a majordownside risk to the forecast: the expiration of tar-iffs might allow a flood of cheaper Canadian lum-ber into the U.S., suppressing domestic prices. Thisdoesn’t seem to have happened. Current expecta-tions are that the countervailing duties imposed onCanadian lumber by the U.S. Department of Com-merce will continue through 2017, though a deal isexpected early in 2018. These duties will supporthigher prices.

Robust growth in U.S. residential improvementsand housing construction would provide muchneeded, if unlikely, high-side potential. This has notyet occurred, despite strong employment growthfor the last two years. Although housing de-mand has picked up there are still a number ofimpediments—persistently stringent lending stan-dards, a continued tough labor market for youngerworkers, student loan debt, and general economicand social malaise—most of which are easing, butnone of which show signs of completely abating justyet. Additionally, there are a number of supply sideimpediments constraining construction growth, pri-marily a lack of skilled labor and a lack of readilybuildable land.

In late 2015, China again instituted a ban on geo-duck imports from the Pacific Northwest due to par-alytic shellfish poison (PSP) and arsenic concerns.However, once again, this didn’t appear to impactprices or harvest activity. In late February 2016, theWashington Department of Health posted an arti-cle saying that China had lifted the ban and it listedthe areas cleared for geoduck export to China. It isentirely possible that China could re-enact a moreforceful ban on geoduck that would have a dra-matic effect on geoduck prices, and therefore rev-enue.

Additionally, friction between geoduck purchasersand divers could disrupt the market, though theseseem to have settled. As always in the geoduck fish-eries, PSP closures create uncertainty around har-vest volumes as well.

Finally, it is unclear how long U.S. economic growthcan continue in the absence of coherent, growth-driven federal economic policies.

II

Table 1: November 2017 Forecast by Source (millions of dollars)Timber Sales FY 14 FY 15 FY 16 FY 17 FY 18 FY 19 FY 20 FY 21

Volume (mmbf) 497 473 545 520 500 500 500 500Change - - - -% Change 0% 0% 0% 0%

Price ($/mbf) 356 348 285 346 363 350 340 340Change $ - $ - $ - $ -% Change 0% 0% 0% 0%

Value of Timber Sales 177.2 164.5 155.3 179.8 181.7 175.0 170.0 170.0Change $ - $ - $ - $ -% Change 0% 0% 0% 0%

Timber Removals

Volume (mmbf) 471 449 490 493 589 597 532 515Change (50) 19 20 15% Change -8% 3% 4% 3%

Price ($/mbf) 323 359 338 313 314 341 346 343Change (3.7) 2.3 (4.8) 0.6% Change -1% 1% -1% 0%

Timber Revenue 152.1 161.4 165.7 154.2 185.3 203.6 184.2 176.8Change (18.2) 7.7 4.3 5.5% Change -9% 4% 2% 3%

Upland Leases

Irrigated Agriculture 6.7 7.8 8.7 9.1 8.7 8.5 8.5 8.5Change 0.2 - - -% Change 2% 0% 0% 0%

Orchard/Vineyard 9.4 8.3 8.2 8.1 8.3 8.0 8.0 8.0Change 0.3 - - -% Change 4% 0% 0% 0%

Dryland Ag/Grazing 7.4 5.0 5.2 5.6 5.6 6.2 6.2 6.2Change - - - -% Change 0% 0% 0% 0%

Commercial 9.6 8.2 9.0 9.7 9.9 9.4 9.4 9.4Change 0.5 - - -% Change 5% 0% 0% 0%

Other Leases 8.8 9.4 10.5 10.7 9.2 10.0 10.1 10.2Change (0.2) - - -% Change -2% 0% 0% 0%

Total Upland Leases 41.9 38.6 41.6 43.1 41.7 42.1 42.2 42.3Change 0.8 - - -% Change 2% 0% 0% 0%

Aquatic Lands

Aquatic Leases 10.5 10.9 11.1 10.8 10.2 10.2 10.2 10.2Change (0.2) (0.2) (0.2) (0.2)% Change -2% -2% -2% -2%

Geoduck 22.1 21.0 14.5 27.9 22.4 17.7 17.8 18.1Change 1.7 - - -% Change 8% 0% 0% 0%

Aquatic Lands Revenue 32.7 31.9 25.6 38.7 32.6 27.9 28.0 28.3Change 1.5 (0.2) (0.2) (0.2)% Change 5% -1% -1% -1%

Total All Sources 226.6 231.9 232.9 236.1 259.7 273.6 254.4 247.3

Change (15.9) 7.5 4.1 5.3% Change -6% 3% 2% 2%

III

Table 2: November 2017 Forecast by Fund (millions of dollars)Management Funds FY 14 FY 15 FY 16 FY 17 FY 18 FY 19 FY 20 FY 21

041 RMCA - Uplands 33.2 30.4 36.0 33.7 41.5 45.1 41.7 40.4Change (2.2) 1.5 3.4 3.4% Change -5% 3% 9% 9%

041 RMCA - Aquatic Lands 14.8 14.4 11.3 17.9 14.8 12.4 12.5 12.6Change 0.8 (0.1) (0.1) (0.1)% Change 6% -1% -1% -1%

014 FDA 19.6 23.2 22.8 22.0 22.5 25.5 23.9 23.1Change (2.7) 0.8 0.3 0.7% Change -11% 3% 1% 3%

Total Management Funds 67.6 68.0 70.2 73.6 78.8 83.0 78.0 76.1Change (4.1) 2.2 3.6 4.0% Change -5% 3% 5% 6%

Current Funds

113 Common School Construction 56.6 50.4 59.7 51.8 65.1 71.3 67.4 65.9Change (3.7) 2.3 (1.1) (0.6)% Change -5% 3% -2% -1%

999 Forest Board Counties 52.0 64.8 55.3 58.5 59.8 63.9 58.8 56.8Change (6.2) 1.7 1.1 1.7% Change -9% 3% 2% 3%

001 General Fund 2.2 1.8 4.1 2.6 2.3 3.7 3.7 3.7Change (0.4) 0.2 (0.0) 0.1% Change -15% 5% -1% 3%

348 University Bond Retirement 1.8 2.8 1.8 1.8 2.9 2.5 2.1 1.9Change (0.0) (0.1) 0.1 (0.0)% Change -1% -5% 3% 0%

347 WSU Bond Retirement 1.7 1.8 1.4 1.7 1.7 1.7 1.7 1.7Change 0.0 - (0.0) (0.0)% Change 1% 0% -2% -2%

042 CEP&RI 5.5 5.2 3.1 4.1 4.1 4.8 4.4 4.3Change (0.1) (0.0) (0.0) (0.0)% Change -2% 0% -1% 0%

036 Capitol Building Construction 6.7 4.9 6.7 8.2 8.7 9.8 8.6 8.2Change 0.3 1.0 0.1 0.0% Change 3% 11% 1% 0%

061/3/5/6 Normal (CWU, EWU, WWU, TESC) School 0.2 0.1 0.1 0.1 0.2 0.2 0.2 0.2Change 0.0 - (0.0) (0.0)% Change 1% 0% -3% -3%

Other Funds 1.5 0.5 0.1 0.0 1.3 0.6 0.2 0.2Change (0.5) 0.0 0.1 0.0% Change -26% 3% 61% 3%

Total Current Funds 128.1 132.4 132.2 129.0 146.2 158.4 147.2 142.8Change (10.6) 5.0 0.1 1.3% Change -7% 3% 0% 1%

(Continued)

IV

Table 3: November 2017 Forecast by Fund (millions of dollars), cont’dAquatic Lands Enhancement Account FY 14 FY 15 FY 16 FY 17 FY 18 FY 19 FY 20 FY 21

02R 17.9 17.4 14.2 20.8 17.8 15.5 15.6 15.7Change 0.7 (0.1) (0.1) (0.1)% Change 4% -1% -1% -1%

Permanent Funds

601 Agricultural College Permanent 3.5 4.1 7.6 4.6 6.1 5.5 4.4 4.0Change (0.5) 0.1 0.3 0.1% Change -8% 3% 7% 3%

604 Normal School Permanent 1.8 1.7 2.4 3.1 3.8 3.7 3.0 2.8Change (0.6) 0.1 0.1 0.0% Change -14% 2% 3% 1%

605 Common School Permanent 0.4 0.7 1.0 0.6 0.3 0.3 0.3 0.3Change - - - -% Change 0% 0% 0% 0%

606 Scientific Permanent 6.1 7.1 5.0 4.1 6.1 6.5 5.4 5.0Change (0.8) 0.2 0.1 0.0% Change -12% 3% 2% 0%

607 University Permanent 1.1 0.4 0.2 0.3 0.5 0.6 0.6 0.6Change 0.0 0.0 (0.0) 0.0% Change 3% 0% 0% 2%

Total Permanent Funds 13.0 14.0 16.2 12.6 16.8 16.6 13.7 12.7Change (1.9) 0.4 0.5 0.2% Change -10% 3% 4% 1%

Total All Funds FY 14 FY 15 FY 16 FY 17 FY 18 FY 19 FY 20 FY 21

226.6 231.9 232.9 236.1 259.7 273.6 254.4 247.3Change (15.9) 7.5 4.1 5.3% Change -6% 3% 2% 2%

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Figure 1: Timber Forecast Charts

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Figure 2: Other Uplands Forecast Charts

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Figure 3: Aquatics and Total Forecast Charts

VIII

Contents

Forecast Summary I

Macroeconomic Conditions 1U.S. Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Gross Domestic Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Employment and Wages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Inflation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Interest Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3The U.S. Dollar and Foreign Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Petroleum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

World Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Wood Markets 8U.S. Housing Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Existing Home Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9New Home Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Household Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Housing Starts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Housing Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Housing Affordability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Export Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Timber Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Price Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Lumber Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Log Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Stumpage Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15DNR Stumpage Price Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

DNR Revenue Forecast 17Timber Revenue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Timber Sales Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Timber Removal Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Timber Sales Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Timber Removal Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Timber Removal Revenue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Upland Lease Revenues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Aquatic Lands Revenues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Total Revenues from All Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Some Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Distribution of Revenues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

List of Tables

1 November 2017 Forecast by Source (millions of dollars) . . . . . . . . . . . . . . . . . . . . III2 November 2017 Forecast by Fund (millions of dollars) . . . . . . . . . . . . . . . . . . . . . IV3 November 2017 Forecast by Fund (millions of dollars), cont’d . . . . . . . . . . . . . . . . . V

List of Figures

1 Timber Forecast Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI2 Other Uplands Forecast Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII3 Aquatics and Total Forecast Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII4 U.S. Gross Domestic Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Unemployment Rate and Monthly Change in Jobs . . . . . . . . . . . . . . . . . . . . . . . 26 Employment and Unemployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Labor Market Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 U.S. Inflation Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Trade-Weighted U.S. Dollar Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 Crude Oil Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Lumber, Log and Stumpage Prices in Washington . . . . . . . . . . . . . . . . . . . . . . . 812 Lumber, Log, and DNR Stumpage Price Seasonality . . . . . . . . . . . . . . . . . . . . . . 813 Home Sales and Starts as a Percentage of Pre-Recession Peak . . . . . . . . . . . . . . . . 914 Existing Home Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915 New Single-Family Home Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1016 Housing Starts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1117 Case-Shiller Existing Home Price Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218 Housing Affordability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1219 Log Export Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1320 Log Export Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1321 DNR Composite Log Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1522 DNR Timber Stumpage Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1623 Forecast Timber Sales Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1724 Forecast Timber Removal Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1725 Forecast Timber Sales Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1826 Forecast Timber Removal Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1827 Forecast Timber Removal Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1828 Forecast Timber Removal Revenue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1929 Forecast Upland Lease Revenue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2030 Aquatic Lands Revenues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2131 Geoduck Auction Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2132 Total Revenues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Acronyms and Abbreviations

bbf Billion board feetBLS U.S. Bureau of Labor StatisticsCAD Canadian dollarCNY Chinese yuan (renminbi)CPI Consumer Price IndexCY Calendar Year

DNR Washington Department of Natural ResourcesECB European Central BankERFC Washington State Economic and Revenue Forecast CouncilFDA Forest Development AccountFEA Forest Economic AdvisorsFed U.S. Federal Reserve Board

FOMC Federal Open Market CommitteeFY Fiscal YearGDP Gross Domestic ProductHMI National Association of Home Builders/Wells Fargo Housing Market IndexIMF International Monetary Fund

mbf Thousand board feetmmbf Million board feetPPI Producer Price IndexQ1 First quarter of year (similarly, Q2, Q3, and Q4)QE Quantitative Easing

RCW Revised Code of WashingtonRISI Resource Information Systems, Inc.RMCA Resource Management Cost AccountSA Seasonally AdjustedSAAR Seasonally Adjusted Annual Rate

TAC Total Allowable CatchUSD U.S. DollarWDFW Washington Department of Fish and WildlifeWWPA Western Wood Products AssociationWTO World Trade Organization

Preface

This Economic and Revenue Forecast projects rev-enues from Washington state lands managed by theWashington State Department of Natural Resources(DNR). These revenues are distributed to manage-ment funds and beneficiary accounts as directed bystatute.

DNR revises its Forecast quarterly to provide up-dated information for trust beneficiaries and stateand department budgeting purposes. Each DNRForecast builds on the previous one, emphasizingongoing changes. Each re-evaluates world andnational macroeconomic conditions, and the de-mand and supply for forest products and othergoods. Finally, each assesses the impact of theseeconomic conditions on projected revenues fromDNR-managed lands.

DNR Forecasts provide information used in theWashington Economic and Revenue Forecast issued bythe Washington State Economic and Revenue Fore-cast Council. The release dates for DNR Forecastsare determined by the state’s forecast schedule asprescribed by RCW 82.33.020. The table below

shows the anticipated schedule for future Economicand Revenue Forecasts.

This Forecast covers fiscal years 2018 through 2021.Fiscal years for Washington State government beginJuly 1 and end June 30. For example, the currentfiscal year, Fiscal Year 2018, runs from July 1, 2017through June 30, 2018.

The baseline date (the point that designates thetransition from “actuals” to predictions) for DNRrevenues in this Forecast is October 1st, 2017. Theforecast numbers beyond that date are predictedfrom the most up-to-date DNR sales and revenuedata available, including DNR’s timber sales resultsthrough October 2017. Macroeconomic and marketoutlook data and trends are the most up-to-dateavailable as the Forecast document is being writ-ten.

Unless otherwise indicated, values are expressedin nominal terms without adjustment for infla-tion or seasonality. Therefore, interpreting trendsin the Forecast requires attention to inflationarychanges in the value of money over time, separatefrom changes attributable to other economic influ-ences.

Economic Forecast Calendar

Forecast Baseline Date Final Data and Publication Date (approximate)

February 2018 January 1, 2018 February 15, 2018June 2018 May 1, 2018 June 15, 2018September 2018 August 1, 2018 September 15, 2018November 2018 October 1, 2018 November 15, 2018

Acknowledgements

The Washington Department of Natural Resources’(DNR) Economic and Revenue Forecast is a collabora-tive effort. It is the product of information providedby private individuals and organizations, as well asDNR staff. Their contributions greatly enhance thequality of the Forecast.

Special thanks are due to those in the wood prod-ucts industry who provided information for DNR’ssurvey of timber purchasers. These busy individu-als and companies volunteered information essen-tial to forecasting the timing of timber removal vol-umes, a critical component of projecting DNR’s rev-enues on behalf of beneficiaries.

Thanks also go to DNR staff who contributed tothe Forecast: Tom Shay, Venice Goetz, Rick Roeder,Katy Mink, Tom Heller, Rod Rennie, Keith Jones,Janet Ballew, Blain Reeves, and Linda Farr. Theyprovided data and counsel, including informationon markets and revenue flows in their areas of re-sponsibility.

In the final analysis, the views expressed are ourown and may not necessarily represent the views ofthe contributors, reviewers, or DNR.

Office of Finance, Budget, and Economics

Kristoffer Larson, EconomistDavid Chertudi, Lead Economist

MACROECONOMIC CONDITIONS

Macroeconomic Conditions

This section briefly reviews macroeconomic condi-tions in the United States and world economies be-cause they influence DNR revenue—most notablythrough the bid prices for DNR timber and geo-duck auctions and lease revenues from managedlands.

U.S. Economy

Gross Domestic Product

Since the end of the Great Recession of 2008 and2009, during which GDP declined in five out of sixquarters, GDP growth has averaged a weak 2.1 per-cent on a real annualized basis (Figure 4). This ismarkedly less than the annualized average of 3.2percent over the previous 50 years (1960-2009).The Great Recession set back economic growthand seriously harmed many sectors of the econ-omy, with especially lasting effects on employmentand wages.

Figure 4: U.S. Gross Domestic Product

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The pattern of stagnant GDP growth was widelypredicted to break in 2014, then again in 2015 andyet again in 2016. However, as each year progressedexpectations were repeatedly reduced. The patternof reduced expectations was particularly stark in

2016, with analysts dropping forecasts from around3.0 percent to around 2.5 percent at the beginningof the year, then to below 2.0 percent as first quar-ter growth disappointed. In the end, GDP growthin those years was disappointing, with only 2.6, 2.9,and 1.5 percent growth respectively.

Growth has been forecast to return to the mid-twopercent range for 2017 and outlying years, to-dateGDP growth suggests that 2017 will be in the low-two percent range. Predictions for 2017 and out-lying years GDP growth are perhaps more uncer-tain than in previous years because it is still unclearwhat the economic and trade policies will look likeunder the current U.S. administration.

Employment and Wages

The U.S. headline unemployment rate declinedfrom 5.7 percent in January 2015 to 4.9 percentin January 2016 and 4.8 percent in January 2017(Figure 5). The unemployment rate ranged between4.7 and 5.0 percent through the 2016 and ended atthe lower end of that range in December. This iswell down from a high of 10.0 percent in October2009 and is below the average unemployment rateof 5.2 percent from 2001-2006. The unemploymentrate has averaged 4.4 percent through October of2017 and has been trending downward. It currentlystands at 4.1 percent. In general, analysts expect theunemployment rate to remain in the four-five per-cent range for the next couple of years, while theFOMC has a range in the high-three to low-fourpercent through 2019.

Job growth slowed in 2016, with around 187,000jobs created per month compared to 226,000 permonth in 2015. This slowdown was generally ex-pected and is consistent with an economy operat-ing nearer to its long-term capacity; on average,about 169,000 jobs have been added each monthto October this year.

The unemployment rate is a useful indicator be-cause it gives insight into slack in the labor mar-ket; that is, how many people are available to workbefore job growth starts driving problematic infla-tion. The labor market is the driving force behind

Page 1 of 23 DNR Economic & Revenue Forecast


consumption, which constitutes about 70 percentof GDP and naturally extends to the demand forhousing, which is the major driver of U.S. timberdemand. Data and anecdotes abound that showthat one of the major effects of high unemploymentrates, particularly among young adults, is lower de-mand for housing as more people live with theirparents or take on housemates.

Figure 5: Unemployment Rate and Monthly Changein Jobs

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Although the unemployment rate has declined andis below the long term average, it has not yet trans-lated into strong wage growth, which is likely a pre-requisite for broader economic improvement andan increase in the demand for housing. One pos-sible reason for this is that the headline unemploy-ment rate may be underestimating the number ofpeople willing to work. During the 2008-09 re-cession the number of people who were underem-ployed or marginally attached to the workforce in-creased dramatically. Additionally, from the begin-ning of the recession to mid-2015 the labor forceparticipation rate declined significantly, falling bythree percentage points to 63 percent, possibly be-cause workers left the labor force after they were

unable to find jobs.

The U-6 is an alternative measure of unemploy-ment that includes involuntarily part-time employ-ment and marginally attached workers, who are notincluded in the headline unemployment rate butwho, nevertheless, are likely to be looking for workand would benefit from better job prospects. TheU-6 has declined from a high of 17.1 percent in 2010to a low of 7.9 percent in October 2017. This is justlower than the average of 9.1 percent from 2001-2006 (Figure 6). The decline in the year-on-yearU-6 is the result of a drop in all three of its compo-nents.

Figure 6: Employment and Unemployment

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Reductions in the labor force participation ratehelped move the unemployment rate and the U-6 lower roughly through January 2014 (Figure 7).Since then the rate has remained relatively stablebetween 62.4 and 63.0 percent and has averaged62.8 percent. The decline in the labor force par-ticipation rate is an important confounding factorwhen examining the unemployment rate and is akey consideration when forecasting whether an in-crease in employment will trigger an increase inwages and inflation. If there are many people wait-

DNR Economic & Revenue Forecast Page 2 of 23


ing to search for employment until jobs are easierto find—such as when people stay out of the laborforce and the participation rate declines—then asemployment grows, more people will enter the la-bor force and there will be little or no pressure onwages despite a low unemployment rate. However,if people are not in the labor market for other rea-sons, then the unemployment rate is a more accu-rate reflection of the labor pool. If the latter is thecase, then a decrease in the rate means that thereare fewer people looking for work, so in order tofill jobs companies will have to compete for labor,pushing up wages.

Figure 7: Labor Market Indicators

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The drop in the participation rate since 2008 sug-gests that the recession itself caused people to leavethe labor market, and implies that they may returnwhen things look a bit better. However, FederalReserve analysts have suggested that the recent de-cline in participation may be part of a longer-termtrend starting in the late 1970s and pausing duringthe 1990s, not as a result of the recession. Indeed,according to statistics released by the Federal Re-serve Bank of Atlanta, many of those dropping outof the labor force can’t or don’t want to work.

Inflation

Aside from a short period in 2012, inflation hasbee below the FOMC’s target since the recession in

2008. Similarly to GDP forecasts, inflation fore-casts have been consistently too high, with eachyear predicted to break the cycle of weak inflation,only to disappoint at the year progresses. (Fig-ure 8).

For policy purposes, the FOMC uses the core Per-sonal Consumption Expenditures (PCE) index asthe guiding measure of inflation, which removesthe more volatile fuel and food prices. This mea-sure shows long-term inflation at or below the twopercent target since September 2008. Core PCEgrowth averaged 1.4 percent in 2015 and 1.7 percentin 2016. The December 2016 FOMC projected arange from 1.7-2.0 core PCE inflation for 2017, butthat was reduced slightly in the June 2017 meetingto a range of 1.6-1.8 percent. PCE inflation throughOctober has averaged 1.7 percent.

The consensus among forecasters, including theFOMC, is that core inflation will remain at or belowtwo percent through 2019.

Figure 8: U.S. Inflation Indices

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Interest Rates

Seldom in U.S. history has it been so inexpensive toborrow money for so long. From December 2008 toDecember 2015, the Federal Reserve held the fed-eral funds rate in the 0.0-0.25 percent range. Dur-ing that time the Fed pledged to keep the rates nearzero until it judged that there had been sufficient



progress toward its dual-mandate of maximum em-ployment and two percent inflation.

In December 2015, the FOMC raised interest ratesto 0.25-0.5 percent after determining that sufficientprogress had been made in the recovery of employ-ment and inflation and, importantly, that there wasa sufficiently strong outlook to begin lifting interestrates from their historic lows. From the December2015 rate rise, the FOMC indicated that they ex-pected a median federal funds rate of 1.4 percentin 2016, which would have been four rate increasesof about 0.25 percent. However, this didn’t hap-pen due to slower than expected inflation and wagegrowth. In December 2016 the FOMC raised ratesagain to 0.5-0.75 percent. In March and again inJune 2017, the FOMC also increased the rate by0.25 percent, leading to current rates of 1.00-1.25percent. These increases were widely expected be-cause the FOMC carefully prepared markets for itwith each successive meeting statement.

An increase in interest rates will generally slowdown economic growth—business investment slowsdown because borrowing money becomes more ex-pensive, so job and wage growth slow down (con-straining consumption). Similarly, it becomes moreexpensive for consumers to borrow, impeding de-mand in the housing and auto markets. In nor-mal times, a decrease in interest rates will ex-pand investment, employment, wages, and con-sumer credit. The question of whether to raise in-terest rates is important because it is the key toolof monetary policy.

The U.S. Dollar and Foreign Trade

The trade-weighted U.S. dollar index has climbeddramatically since 2014. Through 2015 and 2016this was largely due to the relative strength of theU.S. economy, which, although fairly weak, wasgrowing faster than most other advanced countries.Although the value of the U.S. dollar was below its2015 peak for most of 2016, the results of the U.S.presidential election pushed the exchange rate wellabove its previous high. However, that boost wasshort-lived; since then, the dollar has dropped backaround its 2015 averaged - though this is still al-

most 15 percent higher than the value through mostof 2014 (Figure 9).

Figure 9: Trade-Weighted U.S. Dollar Index

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ollarIndexValue

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Importantly, a rising dollar means that timber andlumber from the Pacific Northwest become moreexpensive for international buyers and importedtimber and lumber become less expensive. This willtend to suppress local prices and DNR’s timber andagricultural revenues. Wildstock geoduck revenuewill also be negatively affected because geoduck isprimarily marketed abroad.

Foreign trade and access to export markets is veryimportant for DNR revenues. Chinese demand fortimber and lumber have been a major factor sup-porting lumber prices since 2010, even though DNRtimber cannot be exported directly. Additionally,much of the soft white wheat produced in Washing-ton is exported to Asia and a large portion of PNWgeoduck harvested is exported to China.

Given the proposed policies of the new U.S. admin-istration, the upcoming months and years are likelyto be more volatile for foreign trade and presenta large potential downside risk for DNR revenue.Earlier in the year there was a good deal of specula-tion about ‘trade wars’, particularly with China andMexico. However, aside from the duties imposedon Canadian lumber, it doesn’t seem as thoughthere has been much effective movement on inter-national trade. Additionally, it is very unclear how



much is actually at risk. Chinese demand for tim-ber and lumber has waned significantly in the pastthree years, falling from a peak of 4.1 million cu-bic meters in 2011 to 1.9 million cubic meters in2016, and forecasts are predicting that increases indomestic demand will offset the drop in Chinesedemand. However, unless domestic demand wereto expand significantly, there would still be a largedrop in overall demand if China were to turn awayfrom Washington log and lumber exports.

Some analysts argue that access to wheat and otheragricultural export markets are not in any seriousdanger because our largest trading partners are de-pendent upon imports to satisfy their demand andfood prices in developing countries are highly po-litical. However, that doesn’t mean that they aren’table to preferentially purchase from U.S. competi-tors, particularly Australia, which is the world’slargest exporter of soft white wheat.

Finally, China is apparently the primary market forgeoducks (there is very little information about thegeoduck market, so much of our understanding isanecdotal), so an increase in geoduck prices in theChinese market could have a large impact on thatprogram. However, China has already initiated twobans on geoduck from the Pacific Northwest and forreasons that are unclear, neither ban had an appre-ciable affect on prices—so its possible that geoduckdemand is fairly inelastic, that is, it won’t drop verymuch despite large changes in price.

Petroleum

Crude oil and its derivatives strongly affect pro-duction, transportation, and consumption in theworld and U.S. domestic economies. Prices forBrent crude oil plummeted from $108/barrel in Jan-uary 2014 to $30/barrel in January 2016, a 70 per-cent drop. Prices increased through 2016 but fellthrough much of 2017 to-date, except September,when they jumped to $56/barrel - it’s highest pricesince July 2015.

Broadly, a drop in oil prices acts like a tax cut forconsumers and can encourage consumption. How-ever, data suggest that households initially saved

the windfall or paid down debt instead of spend-ing it, with no noticeable increase in consump-tion. Additionally, the drop was sudden and severeenough that it has undermined business investmentin oil production, creating another drag on eco-nomic growth.

All other things being equal, lower petroleumprices will lower diesel fuel prices and will maketransportation-sensitive industries—such as PNWlogging and agriculture—more competitive in in-ternational markets. However, all other things arenot equal: as discussed above, the U.S. dollar hasrisen dramatically and will make PNW timber moreexpensive internationally. These two forces are op-posing and it is unclear which will be more influen-tial on PNW natural resource exports.

Figure 10: Crude Oil Prices

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World Economy

Europe

Forecasts for the U.S. economy often cite Europe’songoing financial crisis and weak economic perfor-mance as a significant downside risk. The EU (28countries) is the fourth largest trading partner ofthe U.S. and, as a whole, was hammered by theGreat Recession, collectively suffering a 4.5 per-cent contraction in 2009. This was followed bytwo years of slow growth, and another year of con-traction. After no growth in 2013, 2014 saw real



EU GDP growth of 1.3 percent—finally surpassing2007’s GDP in real terms.

Politically, Europe appears to have become less sta-ble. This political turmoil started most visibly withthe possibility of a Greek exit from the EU in mid-2015 (Grexit), continued with the UK’s 2016 voteto leave the EU (Brexit), and remains with the in-crease in support for nationalist political parties op-posed to trade, thought as yet these parties have notbeen given power in the most influential countries.Markets have calmed down after the turmoil of theGrexit and Brexit vote, but the implementation ofthe Brexit will likely have a negative effect on theeconomies of both the UK and the EU and intro-duce further uncertainty.

Weakness and uncertainty in Eurozone economiesmeans reduced demand for U.S. exports, but it hasbeen difficult to identify specific tangible effects onthe U.S. economy.

China

China is a major export market for logs, lumberand geoduck from the Pacific Northwest. Since2011, between 50 and 60 percent of the softwoodlog exports leaving the Seattle and Columbia RiverCustoms District have gone to China and China is(anecdotally) the primary export market for Wash-ington’s geoduck. Changes to the Chinese econ-omy can have a dramatic impact on the prices forlogs, lumber, and geoduck in the Pacific North-west.

China’s GDP and employment weathered the globaleconomic and financial crises better than mostother economies. However, that resilience is prov-ing to be illusory, as the costs of propping up in-vestment and maintaining significant political con-trol over the economy mount and the likelihoodof a dramatic slowdown increase. Already, Chi-nese GDP growth has slowed from 10.4 percent in2010 to 6.9 percent in 2015 and 6.3 percent in2016.

There is growing concern that Chinese GDP growthwill fall much lower, possibly even into recession.This risk is mostly due to the prominence of invest-

ment as a component of GDP, the huge amount ofdebt in the country, and the way that debt is held.Household and corporate debt (to non-financialcorporations) ballooned from about 110 percent ofGDP in 2008 to over 190 percent in 2014, and muchof it is linked to real estate. Investment comprisesalmost 50 percent of China’s GDP. At those lev-els of debt a slowdown in an economy can leadto a drop in income and an inability to servicedebt en-masse, potentially leading to a debt cri-sis that would undermine that investment and havea tremendous impact on China’s GDP.

Another source of uncertainty is the current U.S.administration, which has been critical of tradewith China. China is particularly vulnerable tochanges in access to international markets, particu-larly the U.S., with exports making up 25 percent ofGDP and a large proportion of employment depen-dent upon labor-intensive export industries. Poli-cies targeting Chinese imports could be very dam-aging to Chinese GDP. There is speculation thesetypes of policies would be met with retaliatory ac-tion from China, which would likely undermine de-mand for many of DNR’s revenue-generating prod-ucts.

Japan

Japan is another major export market for the Pa-cific Northwest—importing around 35 percent ofthe softwood logs exported from the Seattle andColumbia River customs districts since 2012. Un-fortunately, Japan’s growth has stagnated since theearly 1990s after a stock market and property bub-ble bust trapped the economy into a deflationaryspiral. After his election in late 2012, JapanesePrime Minister Shinzo Abe began a fairly boldcombination of economic policy moves, dubbed‘Abenomics’, in an attempt to revitalize Japan’seconomy.

Although Abenomics was initially well received, ithasn’t been able to increase inflation or make a not-icable impact on GDP. In January 2016, the Bankof Japan added negative interest rates to the mix ofAbenomics policies and quantitative easing, hop-ing to spur spending and force inflation and GDP



higher. However, this hasn’t been effective.

While the Japanese economy hasn’t pulled out ofslow growth, it does not appear to be in any dangerof a recession or slower growth, so it is unlikely tobe a source of risk for timber prices.


WOOD MARKETS

Wood Markets

Over the past decade, timber stumpage revenuehas constituted about 70 percent of total DNR rev-enues. DNR is, therefore, vitally concerned withunderstanding stumpage prices, log prices, lumberprices, and the related supply and demand dynam-ics underlying all three. This section focuses onspecific market factors that affect timber stumpageprices and overall timber sales revenues generatedby DNR.

Figure 11: Lumber, Log and Stumpage Prices inWashington

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In general, timber stumpage prices reflect demandfor lumber and other wood products, timber supply,and regional lumber mill capacity. There is a con-sistent, positive relationship between log prices andDNR’s stumpage prices, despite notable volatility instumpage prices (Figure 11). High log prices makeaccess to logs more valuable and increase pur-chasers’ willingness to pay for stumpage (the rightto harvest). Volatility in stumpage prices arises notonly from log prices, but also from the volume oflumber and logs held in mills’ inventories and fromDNR-specific issues, such as the quality and type

of the stumpage mix offered at auction, the region,and the road-building requirements of a particularsale.

The relationship between lumber and log pricesis less consistent. Lumber prices are significantlymore volatile and both the direction and size ofprice movements can differ from log prices. This isdue to both demand- and supply-side factors. Onthe demand side, mills will often have an inventoryof logs in their yards, as well as an inventory of‘standing logs’, so they do not always need to bid upstumpage prices to take advantage of high lumberprices. From the supply side, land owners do notoften need to sell their timber, so when prices falltoo far, they can withhold supply and allow theirtrees to grow and increase in quality.

Figure 12: Lumber, Log, and DNR Stumpage PriceSeasonality

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There are differences in price seasonality betweenlumber, logs, and stumpage, as illustrated in Fig-ure 12. These prices are affected by a degree ofseasonality that is largely the result of when eachof these commodities will be used. For instance,lumber prices tend to peak in spring, when hous-ing construction picks up, and decline through fallas demand wanes, while stumpage prices tend tobe highest in January-March, when harvesters arelining up harvestable stock for the summer. DNRstumpage price volatility is also affected by the fire-fighting season and the quality of the stumpage


WOOD MARKETS

mix, which varies throughout the year but tendsto be worse from July through September.

U.S. Housing Market

This section continues with a discussion of the U.S.housing market because it is particularly importantto overall timber demand in the U.S.

New residential construction (housing starts) andresidential improvements are major components ofthe total demand for timber in the U.S. Historically,these sectors have constituted over 70 percent ofsoftwood consumption—45 percent going to hous-ing starts and 25 percent to improvements—withthe remainder going to industrial production andother applications.

Figure 13: Home Sales and Starts as a Percentageof Pre-Recession Peak

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The crash in the housing market and the follow-ing recession drastically reduced demand for newhousing, which undermined the total demand forlumber (Figure 13). Since the 2009-11 trough, theincrease in housing starts has driven an increase inlumber demand, though not to nearly the extent ofthe peak. Prolonged growth in starts is essentialfor a meaningful increase in the demand for lum-ber.

Housing demand appears to be growing afterstalling through late 2014, but has been broadly

subdued due to tight lending standards and in-creasing prices at the same time as stagnant or de-clining real wages for much of the population. Al-though lending standards have relaxed a little andthe labor market is tightening, these improvementsare happening very slowly.

Figure 14: Existing Home Sales

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Existing Home Sales

Existing home sales plummeted during the reces-sion from around 6.5 million (SAAR) in 2006 to alow of around 4.1 million in 2008. They rose toaverage 4.6 million (SAAR) in 2015, an increase onthe 4.3 million average of 2014 (Figure 14). Therewere about 4.8 million sales (SAAR) in 2016, just abit shy of ’normal’ annual sales of around 5 millionhomes. Through October 2017, annualized sales ofexisting single family homes has averaged 4.9 mil-lion.

Changes in inventory can be a useful signal aboutthe current relationship between supply and de-mand. A decreasing inventory suggests that de-mand is outstripping supply, which should put up-ward pressure on prices and encourage more homesto be listed or built. Single-family inventory hasranged between 1.6 and 2.2 million homes, withclear seasonal influences. Inventories built up in thebeginning of 2016 but have fallen since then and, asof October, are well below historical norms: there


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are currently 1.7 million homes in inventory, com-pared to a little over 2 million homes in inventoryprior to the housing market crash in 2006.

After house prices fell in the recession, private in-vestors moved into depressed housing markets andpurchased large numbers of low-priced foreclosedresidential properties. These investors have helpeddrive demand and may have set a floor under sev-eral key urban housing markets. There has beensome concern among analysts about the potentialimpact on house prices if investors were to be-gin selling en-masse, thereby increasing the hous-ing supply while demand continues to be weak.However, without significant potential returns fromother investments, there seems little chance of amass sell-off.

Figure 15: New Single-Family Home Sales

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Unsurprisingly, new home sales also plummetedduring the recession, reaching a record low of306,000 (SAAR) in 2011 before beginning a slowrise (Figure 15). New home sales increased from440,000 (SAAR) in 2014 to an average of 502,000in 2015. The monthly sales for 2016 averaged561,000 homes, an improvement compared to 2015(which averaged 500,000 homes over the same pe-riod), but still well below the long-term (1963-2010)‘normal’ rate of 678,000 sales per year. New homesales through October 2017 averaged an annualized

609,000.

As low as new home sales fell, new home construc-tion fell even lower from early 2007 through mid-2011, causing the inventory of newly built homes forsale to decline over the period. After bottoming outin July 2012, the inventory of new homes has creptup as construction slightly outpaced sales.

Household Formation

Household formation (the growth in the number ofhouseholds) is a key component of housing demandand a major driver of U.S. housing starts. Due tothe job and income losses and to the greater fi-nancial precarity that the recession created, house-hold formation fell as people shared housing andmany younger people, who were hit especially hard,moved back in with their parents. Net immigrationfrom Mexico also approached zero following the re-cession, and may have actually been negative, con-tributing to slowing household formation.

The drop in household formation and the conse-quent reduction in demand for home purchasescontributed to the surge in the inventory of avail-able housing units and significant drop in housingstarts. Historically, U.S. household formation hasranged between 1.2 and 1.3 million per year; follow-ing the recession, household formations droppeddramatically to average 0.7 million per year from2009-2014.

An important concept frequently discussed in re-lation to household formation is that of ‘pent-up’demand—the demand for housing from those whowish to form households, but are currently unableto because of employment, earnings, or credit el-igibility issues. Much of the discussion from an-alysts in the past several years has been abouta large, and growing, pent-up demand as moreyoung adults want to move out and create theirown households. Analysts have consistently overes-timated its impact on the housing market, repeat-edly predicting a strong rebound in household for-mation and housing starts that has yet to emerge.In other words, pent-up demand has so far failedto become real demand, largely because of issues


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with employment, wages, credit requirements, andaffordability.

Forecasts for household formation are for a returnto the 1980-2007 average of a bit over 1.4 millionformations per year. Looking forward, householdformation will depend on both the continued re-covery in the U.S. labor market—more than just jobgrowth, but also real wage growth—improvementsin housing affordability and mortgage access, andnet immigration.

Housing Starts

In April 2009, U.S. housing starts fell to record lowssince the Census Bureau began tracking these datain 1959. U.S. housing starts picked up in 2011 andcontinued to rise, largely because of increases inmulti-family starts. Single-family starts were moreor less flat after the recession through 2012, buthave been rising slowly since (Figure 16).

Figure 16: Housing Starts

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Since the recession, total housing starts have beenmade up of a larger portion of multi-family unitsthan in the past. This is pertinent because multi-family structures use much less lumber than single-family houses per unit, so the slow recovery in over-all starts has had a more muted effect on timberprices than historical increases. However, it is notclear how long multi-family starts will drive total

starts: in 2016 multi-family starts were lower thanin 2015, 385,000 and 395,000 starts respectively,while single family starts increased from 718,000to 783,000 (SAAR). In 2017, multi-family starts areon track for a further decline, averaging 353,000starts (annualized) through August, while single-family starts have averaged 837,000.

Starts totaled around 1.0 million and 1.1 million(SAAR) in 2014 and 2015, overcoming low firstquarter starts that were dragged down by severeweather in both years. Housing starts in 2016totaled 1.2 million. Continued improvements inhousehold formations will increase demand anddrive an increase in starts, though it is unclear howlong it will take before formations increase. Addi-tionally, a recovery in house prices should facilitatethe ‘move-up’ market. An increase in the move-upmarket combined with low total inventories con-straining the supply of existing housing should startincreasing prices and provide incentives to buildmore houses; again, this is likely to be constrainedby how much people can afford, so wages and lend-ing standards will play a significant role.

Builder confidence is no longer an impediment tohousing starts, as estimates of confidence are con-sistent with housing starts of over 1 million. How-ever, there are significant supply impediments, suchas the shortage of buildable lots and permit de-lays. Given the lead time necessary to build houses,these are likely to cause volatility in both prices andsupply.

Housing Prices

U.S. housing experienced six unprecedented yearsof falling or flat prices following the recession.House prices started rising again only in 2012 aseconomic and employment indicators continued toimprove. Figure 17 charts the seasonally adjustedS&P/Case-Shiller Home Price Index for the 20-city composite, which estimates national existinghome price trends. The 20-city composite indexhas increased in most months since bottoming outin January 2012—its lowest point since October2002.


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Seattle house prices are growing much faster thannational prices, increasing 12.5 percent year-on-year as of August, compared with 5.9 percent na-tionally. When Seattle prices bottomed in February2012—their lowest point since June 2004—the av-erage existing house in Seattle was worth only 70percent of the May 2007 peak. As of August, theaverage Seattle home was worth over 20 percentmore than its peak price before the recession (innominal terms).

The increase in prices is bringing back more nor-mal foreclosure conditions in which homeownerscan make rational decisions about whether to sell—as opposed to being forced to sell or to remain ‘un-derwater’ to avoid selling at a loss or compromis-ing their credit. However, house prices elsewherein the U.S.—especially in those areas most devas-tated by the foreclosure crisis—have not increasedas quickly as in Seattle.

Figure 17: Case-Shiller Existing Home Price Index

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Housing Affordability

The National Association of Realtors’ (NAR) U.S.Housing Affordability Index is a useful, though im-

perfect, measure of how affordable or attainablehouses are to the average American. Index val-ues increase as affordability increases, and declineas homes become less affordable.

Figure 18: Housing Affordability

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The affordability index is based on house prices,mortgage rates and income. The index increasesif house prices decrease, mortgage rates decreaseor incomes increase. The index is useful becausemovements in house prices, mortgage rates andhousehold income can offset each other so thatit might not be immediately obvious how thosechanges affect the overall house buying power ofthe average household. The index provides aneasy way to assess whether houses are more orless affordable on average. For instance, supposeincomes increase (which will generally increase af-fordability and put upward pressure on the index)but that mortgage rates also increase (which wouldput downward pressure on the index) — while im-perfect, the index provides a consistent method toasses these changes.

The affordability index peaked at a record high of213 in January 2013 and then crashed to 158 in Au-gust of that year—its steepest decline in 30 years—


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on the back of increased interest rates and houseprices (Figure 18). Following that decline the indexrose and fell largely because of seasonal house pricechanges. From August 2013 through May 2017, theindex was been between 153 and 180. However,since March the index has dropped to 149 in Au-gust, largely because house prices and interest ratesincreased while income remained flat.

Export Markets

Although Federal law prohibits export of logs frompublic lands west of the 108th meridian, log exportsstill have a meaningful impact on DNR stumpageprices. Exports compete with domestic purchasesfor privately sourced logs and strong export com-petition pulls more of the supply from the domesticmarket, thereby raising all domestic prices. How-ever, changes in export prices do not influence do-mestic prices in a one-to-one relationship.

Export prices are almost always higher than do-mestic prices, a difference which is referred to asthe ‘export premium’ (Figure 19). The export pre-mium is primarily due to the characteristics of theexport markets, which can include a demand forhigher quality wood, a high value placed on long-term contracts, and high transaction costs.

Figure 19: Log Export Prices

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Note that the export prices shown in Figure 19 are

weighted by DNR’s typical species mix, not thespecies mix of actual export volumes.

Since 2010, demand from China has been a majorsupport for log and lumber prices in Washington.That demand waned significantly in late 2014 asChina’s economic health wavered, the U.S. dollarappreciated while the value of the euro and rubledropped (making U.S. timber comparatively morecostly), and the Russian tariff on log exports was re-duced. The downward trend in demand continuedthrough 2015, with Douglas-fir log exports down46 percent and hemlock (and other whitewood) ex-ports down 33 percent from 2014 (Figure 20). Ex-ports to China from the Seattle and Columbia-Snake River Customs Districts for both Douglas-firand Hemlock are were 11 percent lower in 2016 than2015, 1.9 million m3, compared to 2.1 million m3 in2015 and 3.2 million m3 in 2014.

Figure 20: Log Export Volume

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The trend of decreased exports to China has con-tinued in 2017, with 0.6 million m3 of Hemlock and0.3 million m3 of Douglas-fir shipped through July,compared to 0.7 million m3 of Hemlock and 0.4million m3 of Douglas-fir through the same timeperiod in 2016.

The export premium is expected to shrink due tostrong demand from recovering domestic marketsand decreased demand from importing countries,China in particular. In the long run, the exportpremium may shrink yet more as West Coast log


WOOD MARKETS

exports face stronger international competition andexport prices are pushed down. Much will dependon supply constraints from key international suppli-ers and transportation constraints from the south-eastern U.S.

Timber Supply

Since the beginning of the recession timber growththroughout the U.S. has generally exceeded tim-ber harvest, increasing the timber inventory. How-ever, strong log exports from the West Coast droveup harvests relative to other parts of the country,so that inventory growth was slower than in otherparts of the country, particularly the U.S. South.Timber growth is expected to continue to exceedharvest through 2017, further increasing invento-ries. The relatively ready availability of timber isunlikely to put significant downward pressure onprices because not all of the timber will enter themarket at once and an expected increase in de-mand for timber, via an improving housing market,will offset the higher supply.

Since the late 1990s British Columbian forests havebeen devastated by the mountain timber beetle,which affected about a third of the province’s timberresources. Typically, timber killed by beetles mustbe harvested within 4 to 10 years so in 2007 thegovernment increased the allowable harvest to en-sure that the dead timber was not wasted, which in-creased British Columbia’s harvestable timber sup-ply. These elevated timber supplies are already de-clining and it’s expected that most of the beetlekill will be unviable by late 2017. The supply fromCanada will be further diminished by Quebec’s al-lowable annual cut being reduced by Bill 57, whichwas implemented in April 2013, and may be ad-ditionally reduced by the ‘North for All’ plan (for-merly Plan Nord).

Price Outlook

Lumber Prices

As shown in Figure 11, lumber prices dropped pre-cipitously from mid-2014 to mid-2015, before lev-eling off. FEA’s coast lumber price peaked at$402/mbf in May 2014, but fell throughout the restof the year to average $376/mbf. This was largelydue to a bitterly cold winter across much of theU.S. which weakened domestic demand, ample lo-cal timber and lumber inventories, and the drop inexport demand from China. Prices in 2015 contin-ued their general downward trend and ended theyear averaging $317/mbf. Prices increased in 2016to average $341/mbf; they’ve averaged $406/mbfthrough September of 2017.

Prices early in 2017 were expected to spike withan anticipated imposition of countervailing and an-tidumping duties on Canadian lumber, which theUS Department of Commerce initiated in April.These additional duties have been expected sincethe end of the Softwood Lumber Agreement (SLA)in October 2015, which governed the quantity ofCanadian lumber imports allowed and duty levelsallowed based on lumber prices. Due to constraintsin the SLA, the U.S. was prevented from bringingany trade action against Canada until 12 October,2016. A petition was filed with the Department ofCommerce and the International Trade Commis-sion in November 2016.

Lumber prices were expected to spiked prior to thenew duties, as lumber buyers increased orders toavoid the new taxes, but also increased after the du-ties were in place because they constrained supply.For the rest of 2017, lumber prices were expectedto be somewhat weaker as buyers draw down on in-ventory in anticipation of the slower building sea-son and a ’gap’ in the countervailing duties1. Thisprice decrease has not happened. Instead, priceshave risen through 2017 from $351/mbf in Januaryto $440/mbf in September.

In the longer run, prices are expected to gener-1Apparently countervailing duties can only be collected for four months, but the International Trade Commission may take

several a couple of months to make a final determination, meaning that there will be some time gap when countervailing dutieswill not be collected.


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ally increase with increased demand, but they maybe more volatile due to the Canadian lumber du-ties.

Log Prices

Figure 21 presents prices for Douglas-fir, hemlock,and DNR’s composite log. The latter is calcu-lated from prices for logs delivered to regionalmills, weighted by the average geographic location,species, and grade composition of timber typicallysold by DNR. In other words, it is the price a millwould pay for delivery of the typical log harvestedfrom DNR-managed lands. The dark green line forthe DNR composite log price on Figure 21 is thesame as the light green line on Figure 11.

Readily visible on the graph is the decline in thepremium for Douglas-fir—due in large part to Chi-nese demand fortifying hemlock prices. Also read-ily visible is the drop in prices from late 2014 toearly 2016. The price of a ‘typical’ DNR log movedup sharply from a two-year plateau in 2013 to$591/mbf in 2014. However, prices declined through2015 to average $521/mbf. The decline in log priceis primarily due to the slowdown in demand fromChina and ample regional supply of both logs andlumber.

Log prices in 2016 increased to average $536/mbf,they’ve averaged $592 through September 2017,and we expect them to remain strong for the rest ofthe year.

Stumpage Prices

Timber stumpage prices are the prices that suc-cessful bidders pay for the right to harvest timberfrom DNR-managed lands (Figure 22). At any time,the difference between the delivered log price andDNR’s stumpage price is equivalent to the sum oflogging costs, hauling costs, and harvest profit (Fig-ure 11). Subtracting the average of these costs fromthe log price line gives us a derived DNR stumpageprice.

Figure 21: DNR Composite Log Prices

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When actual DNR stumpage prices differ signifi-cantly from the derived stumpage prices, a cor-rection is likely to occur. For instance, in 2012actual stumpage prices were generally lower thanstumpage prices inferred from log prices, suggest-ing that an upward market ‘correction’ would beforthcoming. This correction seems to have oc-curred with generally higher stumpage in 2013 and2014. However, the situation reversed in late 2014,when actual DNR stumpage prices were well abovethe inferred stumpage prices.

DNR Stumpage Price Outlook

DNR currently contracts with two forest eco-nomics consulting firms that provide log and tim-ber stumpage price forecasts, as well as valu-able insights into the housing, lumber, and tim-ber markets. By modeling DNR’s historical dataon their price forecasts, we arrive at two alternativestumpage price outlooks (Figure 22, note that theRISI and FEA ‘forecast’ series are both adapted toreflect the species and class characteristics of typi-cal DNR timber; the original series were West Coastaverages, and are not shown).

In previous forecasts, the DNR stumpage price fore-cast represented a weighted middle ground betweenthe two consultants’ outlooks; however, since theSeptember 2015 Forecast we have taken a more pes-


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simistic view with our spot price forecasts. This de-cision appears to have been well founded, as bothconsultants lowered their price forecasts throughFY 16. Even taking into account the large numberof salvage sales in that year, the forecast prices werestill too high.

It is important to note that these are nominal priceexpectations. In real (inflation adjusted) terms,the forecast stumpage prices will still be muchlower than the highs achieved during the housingboom.

Figure 22: DNR Timber Stumpage Price

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DNR REVENUE FORECAST

DNR Revenue Forecast

This Revenue Forecast includes revenue generatedfrom timber sales on trust uplands, leases on trustuplands, and leases on aquatic lands. It also fore-casts revenues to individual funds, including DNRmanagement funds, beneficiary current funds, andbeneficiary permanent funds. Caveats about theuncertainty of forecasting DNR-managed revenuesare summarized near the end of this section.

Figure 23: Forecast Timber Sales Volume

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Timber Revenue

DNR sells timber through auctioned contracts thatvary in duration. For instance, contracts for DNRtimber sales sold in FY 2014 needed to be harvestedbetween three months and four and a half yearsfrom the date of sale, with an average (weighted byvolume) of about 25 months. The purchaser deter-mines the actual timing of harvest within the termsof the contract, which is likely based on perceptionsof market conditions. As a result, timber revenuesto beneficiaries and DNR management funds lagbehind sales.

For the purposes of this chapter, timber that is soldbut not yet harvested is referred to as ‘inventory’or ‘under contract’. Timber volume is added to theinventory when it is sold and placed under con-tract, and it is removed from the inventory whenthe timber is harvested.

Timber Sales Volume

Sales volume forecasts all years are unchanged (Fig-ure 23).

Figure 24: Forecast Timber Removal Volume

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FY 15 was the first year of the new sustainableharvest decade (FY 15 through FY 24) for westernWashington; however, new harvest targets for thethis sustainable harvest decade have not yet beendetermined or approved by the Board of NaturalResources. Without an updated sustainable harvestlimit, annual Westside sales volumes are forecast tobe 450 mmbf for future years. Together with pro-jected Eastside timber sales of 50 mmbf for eachof the next several years, we arrive at a projectedannual timber sales volume of about 500 mmbf forFYs 18-21.


For each Forecast, we survey timber sale purchasersto determine their planned harvest timing for thetimber volume they have under contract at the timeof the survey. This Forecast’s survey, conducted inthe first half of October, indicates that purchasersare planning to harvest 389 mmbf of current inven-tory (688 mmbf) volume in the remainder of thisfiscal year. Combined with harvests to-date throughAugust and harvests expected from remaining salesin the fiscal year, FY 18 removal volume is forecast



to total 589 mmbf—a decrease of 50 mmbf fromthe September prediction.

The volume not harvested in FY 18 is expectedto be harvested in outlying years (see Fig-ure 24).

Figure 25: Forecast Timber Sales Price

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Figure 26: Forecast Timber Removal Price

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Timber Sales Prices

The price results of monthly DNR timber salesare quite volatile (Figure 11). As discussed inthe stumpage price outlook, the DNR sales price(stumpage) forecast uses estimates from two forest

economics consulting firms. Sales price forecasts inoutlying years are unchanged.

Timber Removal Prices

Timber removal prices are determined by salesprices, volumes, and harvest timing. They can bethought of as a moving average of previous tim-ber sales prices, weighted by the volume of auc-tioned timber removed in each time period (Fig-ure 26). Removal prices are decreased slightly inFY 18 due to a shift in the harvest timing of morevaluable timber, which cascades into small removalprice changes in outlying years.

Figure 27: Forecast Timber Removal Value

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Timber Removal Revenue

Figure 27 shows projected annual timber removalrevenues, broken down by the fiscal year in whichthe timber was sold (‘sales under contract’ are al-ready sold as of May 1st, 2017). Revenue estimatesreflect all of the changes described above.

Projections for the 2017-2019 Biennium are $388million, lower by about $11 million than forecast inSeptember (2.6 percent), and $361 million for the2019-2021 Biennium, higher by about $10 million(2.8 percent).



Figure 28: Forecast Timber Removal Revenue

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Upland Lease Revenues

Upland lease revenues are generated primarily fromleases and the sale of valuable materials, other thantimber, on state trust lands (Figure 29). Projectedrevenue from irrigated agriculture is increased by$0.2 million in FY 18 due to higher than expectedrevenue to-date, but unchanged in outlying years.

Orchard and vineyard lease revenues are increasedby $0.3 million in FY 18, again due to higher thanexpected revenue to-date. Commercial lease rev-enues are increased by $0.5 million after a care-ful analysis of all of the commercial leases revealedthat previously forecast was likely too low. Otherleases are dropped by $0.2 million, due to lowerthan expected revenue to-date.

Figure 29: Forecast Upland Lease Revenue

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Aquatic Lands Revenues

Aquatic lands revenues are generated from leaseson aquatic lands and from sales of geoduck. Onaverage, leases account for one-third of the rev-enue while geoduck sales account for the remain-der.

The aquatic lease revenue forecast is reduced for allforecast years due continued lower assessed landvalues undermining revenue growht. This trendshows no signs of abating (Figure 30).

The geoduck revenue forecast for FY 18 has beenincreased to $22 million due to an increase in ex-pected pounds offered for auction (Figure 31).

Figure 30: Aquatic Lands Revenues

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Starting in Q2 2014, our geoduck price forecastswere consistently high and prices seemed to haveentered a period of fairly low volatility. This sug-gested that there may have been some change inthe equilibrium price of geoduck—that the lowerprices weren’t just part of the natural volatility ofthe market, but a fundamental shift in the pricelevel. The consistently higher auction prices sinceAugust 2016, threw that hypothesis into questionand suggested that a new price level was somewhathigher than the average in 2014. However, given thehistorical volatility of the market, it seems impru-dent to increase the outlying years’ auction priceforecasts too much, so the auction price forecast is

unchanged at one standard error below the meanforecasted model.

There are significant downside and upside risks togeoduck revenues, even in the near term, that areimportant to consider but difficult to forecast. Onthe downside:

• Harvests (and therefore revenues) could bedeferred or lost if geoduck beds are closeddue to occurrence of paralytic shellfish poi-son.

• A further slowdown in China’s economicgrowth or a trade war could lower demandfor this luxury export in its largest market.

• In light of recent WDFW surveys of closedsouth Puget Sound geoduck tracts showingdeclining recovery rates, and of evidence ofactive poaching, future commercial harvestlevels may be further reduced.

Figure 31: Geoduck Auction Prices

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Total Revenues from All Sources

Forecast revenues for the 2017-2019 biennium arereduced by $8 million to $533 million, while rev-enues for the 2019-2021 biennium are raised by $10million to $502 million (Figure 32).

Figure 32: Total Revenues

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Some Caveats

DNR strives to produce the most accurate and ob-jective projections possible, based on DNR’s currentpolicy directions and available information. Ac-tual revenues will depend on future policy decisionsmade by the Legislature, the Board of Natural Re-sources, and DNR, as well as on market and otherconditions beyond DNR’s control.

See the Forecast Summary for more details.



Distribution of Revenues

The distribution of timber revenues by trust arebased on:

• The volumes and values of timber in the in-ventory (sales sold but not yet harvested) bytrust;

• The volumes of timber in planned sales forFYs 17-18 by trust, and relative historical tim-ber prices by DNR region by trust; and

• The volumes of timber by trust for FYs 19-21 based on provisional output of the sus-tainable harvest model and relative historicaltimber prices by DNR region by trust.

Since a single timber sale can be worth more than$3 million, dropping, adding, or delaying even onesale can represent a significant shift in revenues toa specific trust fund.

Distributions of upland and aquatic lease revenuesby trust are assumed to be proportional to historicdistributions unless otherwise specified.

Management Fee Deduction. The underlyingstatutory management fee deductions to DNR asauthorized by the legislature are 25 percent or less,as determined by the Board of Natural Resources

(Board), for both the Resources Management CostAccount (RMCA) and the Forest Development Ac-count (FDA). In biennial budget bills, the Legisla-ture has authorized a deduction of up to 30 percentto RMCA since July 1, 2005. In 2015, they autho-rized a deduction up to 31 percent.

At its April 2011 meeting, the Board adopted a res-olution to reduce the RMCA deduction from 30 to27 percent and the FDA deduction from 25 to 23percent. At its July 2011 meeting, the Board de-cided to continue the deductions at 27 percent forRMCA (so long as this rate is authorized by the leg-islature) and at 23 percent for FDA. At its October2011 meeting, the Board approved a resolution toreduce the FDA deduction from 23 to 21 percent.The Board decided in July 2013 to raise the FDAdeduction to 25 percent and the RMCA deductionto 29 percent. In August 2015 the Board raised theRMCA deduction up to 31 percent for the 2015-2017biennium.

The Forecast uses the 31 percent deduction for the2017-2019 and 2019-2021 biennia. This assumesthat the Legislature will approve RMCA deductionsof up to 31 percent.

Given this background of official actions by the leg-islature and the Board, the management fee deduc-tions assumed in this Forecast are:

FY 2018 FY 2019 FY 2020 FY 2021FDA 25 25 25 25RMCA 31 31 31 31


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